Overview

Dataset statistics

Number of variables12
Number of observations3098
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.6 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

id_Provincia is highly overall correlated with Link IndecHigh correlation
Link Indec is highly overall correlated with id_ProvinciaHigh correlation
6 - 10 Mbps is highly overall correlated with 10 - 20 MbpsHigh correlation
10 - 20 Mbps is highly overall correlated with 6 - 10 Mbps and 2 other fieldsHigh correlation
20 - 30 Mbps is highly overall correlated with 10 - 20 Mbps and 1 other fieldsHigh correlation
>=30 Mbps is highly overall correlated with 10 - 20 Mbps and 1 other fieldsHigh correlation
<0.512 mbps is highly skewed (γ1 = 47.09634123)Skewed
id_Provincia has 712 (23.0%) zerosZeros
<0.512 mbps has 2444 (78.9%) zerosZeros
0.512 - 1 Mbps has 2381 (76.9%) zerosZeros
1 - 6 Mbps has 1028 (33.2%) zerosZeros
6 - 10 Mbps has 1398 (45.1%) zerosZeros
10 - 20 Mbps has 1207 (39.0%) zerosZeros
20 - 30 Mbps has 1833 (59.2%) zerosZeros
>=30 Mbps has 2164 (69.9%) zerosZeros
Otros has 2173 (70.1%) zerosZeros

Reproduction

Analysis started2023-07-16 23:00:30.612475
Analysis finished2023-07-16 23:00:49.487081
Duration18.87 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

id_Provincia
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3863783
Minimum0
Maximum23
Zeros712
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:49.548093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.5772314
Coefficient of variation (CV)0.80725825
Kurtosis-1.3507009
Mean9.3863783
Median Absolute Deviation (MAD)8
Skewness0.23017422
Sum29079
Variance57.414435
MonotonicityIncreasing
2023-07-16T20:00:49.672121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 712
23.0%
5 383
12.4%
20 354
11.4%
7 148
 
4.8%
12 147
 
4.7%
21 129
 
4.2%
16 123
 
4.0%
18 97
 
3.1%
13 95
 
3.1%
15 95
 
3.1%
Other values (14) 815
26.3%
ValueCountFrequency (%)
0 712
23.0%
1 2
 
0.1%
2 72
 
2.3%
3 75
 
2.4%
4 63
 
2.0%
5 383
12.4%
6 65
 
2.1%
7 148
 
4.8%
8 63
 
2.0%
9 92
 
3.0%
ValueCountFrequency (%)
23 68
 
2.2%
22 8
 
0.3%
21 129
 
4.2%
20 354
11.4%
19 23
 
0.7%
18 97
 
3.1%
17 79
 
2.6%
16 123
 
4.0%
15 95
 
3.1%
14 54
 
1.7%

id_Localidad
Real number (ℝ)

Distinct2801
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2141.0555
Minimum1
Maximum3993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:49.817095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile207.7
Q11175
median2166.5
Q33153.5
95-th percentile3912.15
Maximum3993
Range3992
Interquartile range (IQR)1978.5

Descriptive statistics

Standard deviation1155.3504
Coefficient of variation (CV)0.53961722
Kurtosis-1.144461
Mean2141.0555
Median Absolute Deviation (MAD)989
Skewness-0.11895884
Sum6632990
Variance1334834.6
MonotonicityNot monotonic
2023-07-16T20:00:49.972122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3964 74
 
2.4%
3181 8
 
0.3%
3140 7
 
0.2%
3070 6
 
0.2%
3173 6
 
0.2%
3203 5
 
0.2%
3232 5
 
0.2%
8 4
 
0.1%
3066 4
 
0.1%
3197 4
 
0.1%
Other values (2791) 2975
96.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 4
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
3993 1
< 0.1%
3992 1
< 0.1%
3991 1
< 0.1%
3990 1
< 0.1%
3989 1
< 0.1%
3988 1
< 0.1%
3987 1
< 0.1%
3986 1
< 0.1%
3985 1
< 0.1%
3984 1
< 0.1%

id_Partido
Real number (ℝ)

Distinct432
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.40316
Minimum0
Maximum446
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:50.138068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.85
Q1121
median231
Q3343
95-th percentile422
Maximum446
Range446
Interquartile range (IQR)222

Descriptive statistics

Standard deviation125.80724
Coefficient of variation (CV)0.54841109
Kurtosis-1.153917
Mean229.40316
Median Absolute Deviation (MAD)111
Skewness-0.11002378
Sum710691
Variance15827.461
MonotonicityNot monotonic
2023-07-16T20:00:50.283108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
369 55
 
1.8%
176 42
 
1.4%
100 38
 
1.2%
77 38
 
1.2%
372 37
 
1.2%
217 34
 
1.1%
71 34
 
1.1%
367 33
 
1.1%
332 31
 
1.0%
336 31
 
1.0%
Other values (422) 2725
88.0%
ValueCountFrequency (%)
0 3
 
0.1%
1 2
 
0.1%
2 2
 
0.1%
3 31
1.0%
4 30
1.0%
5 3
 
0.1%
6 9
 
0.3%
7 4
 
0.1%
8 2
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
446 23
0.7%
445 1
 
< 0.1%
444 1
 
< 0.1%
443 5
 
0.2%
442 3
 
0.1%
441 2
 
0.1%
440 1
 
< 0.1%
439 3
 
0.1%
438 2
 
0.1%
437 7
 
0.2%

Link Indec
Real number (ℝ)

HIGH CORRELATION 

Distinct2679
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38692698
Minimum-1
Maximum94014020
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)2.5%
Memory size24.3 KiB
2023-07-16T20:00:50.432007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile6105058.5
Q110007062
median30098115
Q366117285
95-th percentile86056052
Maximum94014020
Range94014021
Interquartile range (IQR)56110222

Descriptive statistics

Standard deviation29829780
Coefficient of variation (CV)0.77094079
Kurtosis-1.4133305
Mean38692698
Median Absolute Deviation (MAD)23629100
Skewness0.33948856
Sum1.1986998 × 1011
Variance8.8981575 × 1014
MonotonicityNot monotonic
2023-07-16T20:00:50.593008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 77
 
2.5%
6441030 27
 
0.9%
6371010 27
 
0.9%
6427010 15
 
0.5%
6840010 15
 
0.5%
6638040 14
 
0.5%
6028010 12
 
0.4%
6274010 11
 
0.4%
6805010 11
 
0.4%
6091010 10
 
0.3%
Other values (2669) 2879
92.9%
ValueCountFrequency (%)
-1 77
2.5%
2000010 1
 
< 0.1%
6007010 1
 
< 0.1%
6007020 1
 
< 0.1%
6007050 1
 
< 0.1%
6007070 1
 
< 0.1%
6007080 1
 
< 0.1%
6014010 1
 
< 0.1%
6014020 1
 
< 0.1%
6014030 1
 
< 0.1%
ValueCountFrequency (%)
94014020 1
< 0.1%
94014010 1
< 0.1%
94007020 1
< 0.1%
94007010 1
< 0.1%
90119030 1
< 0.1%
90119020 1
< 0.1%
90112030 1
< 0.1%
90112020 1
< 0.1%
90105100 1
< 0.1%
90105080 1
< 0.1%

<0.512 mbps
Real number (ℝ)

SKEWED  ZEROS 

Distinct40
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9280181
Minimum0
Maximum2252
Zeros2444
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:50.735040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum2252
Range2252
Interquartile range (IQR)0

Descriptive statistics

Standard deviation43.331832
Coefficient of variation (CV)22.474806
Kurtosis2380.1996
Mean1.9280181
Median Absolute Deviation (MAD)0
Skewness47.096341
Sum5973
Variance1877.6477
MonotonicityNot monotonic
2023-07-16T20:00:50.872073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 2444
78.9%
1 365
 
11.8%
2 112
 
3.6%
3 49
 
1.6%
4 22
 
0.7%
5 18
 
0.6%
10 13
 
0.4%
8 10
 
0.3%
7 9
 
0.3%
6 8
 
0.3%
Other values (30) 48
 
1.5%
ValueCountFrequency (%)
0 2444
78.9%
1 365
 
11.8%
2 112
 
3.6%
3 49
 
1.6%
4 22
 
0.7%
5 18
 
0.6%
6 8
 
0.3%
7 9
 
0.3%
8 10
 
0.3%
9 4
 
0.1%
ValueCountFrequency (%)
2252 1
< 0.1%
761 1
< 0.1%
266 1
< 0.1%
200 1
< 0.1%
134 1
< 0.1%
89 1
< 0.1%
75 1
< 0.1%
74 1
< 0.1%
42 2
0.1%
39 1
< 0.1%

0.512 - 1 Mbps
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2424145
Minimum0
Maximum1110
Zeros2381
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:51.016990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23
Maximum1110
Range1110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.19878
Coefficient of variation (CV)6.1027686
Kurtosis290.83918
Mean7.2424145
Median Absolute Deviation (MAD)0
Skewness15.444509
Sum22437
Variance1953.5321
MonotonicityNot monotonic
2023-07-16T20:00:51.169012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2381
76.9%
10 358
 
11.6%
20 30
 
1.0%
2 29
 
0.9%
1 19
 
0.6%
3 16
 
0.5%
50 15
 
0.5%
30 14
 
0.5%
4 12
 
0.4%
7 10
 
0.3%
Other values (91) 214
 
6.9%
ValueCountFrequency (%)
0 2381
76.9%
1 19
 
0.6%
2 29
 
0.9%
3 16
 
0.5%
4 12
 
0.4%
5 10
 
0.3%
6 8
 
0.3%
7 10
 
0.3%
8 5
 
0.2%
9 3
 
0.1%
ValueCountFrequency (%)
1110 1
< 0.1%
918 1
< 0.1%
777 1
< 0.1%
768 1
< 0.1%
735 1
< 0.1%
610 1
< 0.1%
523 1
< 0.1%
461 1
< 0.1%
397 1
< 0.1%
390 1
< 0.1%

1 - 6 Mbps
Real number (ℝ)

ZEROS 

Distinct631
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.82214
Minimum0
Maximum2423
Zeros1028
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:51.319061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q3123.75
95-th percentile735.45
Maximum2423
Range2423
Interquartile range (IQR)123.75

Descriptive statistics

Standard deviation281.87892
Coefficient of variation (CV)2.0753532
Kurtosis13.307232
Mean135.82214
Median Absolute Deviation (MAD)12
Skewness3.3152941
Sum420777
Variance79455.725
MonotonicityNot monotonic
2023-07-16T20:00:51.468069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1028
33.2%
1 103
 
3.3%
2 78
 
2.5%
5 74
 
2.4%
3 63
 
2.0%
4 37
 
1.2%
7 31
 
1.0%
6 30
 
1.0%
12 26
 
0.8%
8 25
 
0.8%
Other values (621) 1603
51.7%
ValueCountFrequency (%)
0 1028
33.2%
1 103
 
3.3%
2 78
 
2.5%
3 63
 
2.0%
4 37
 
1.2%
5 74
 
2.4%
6 30
 
1.0%
7 31
 
1.0%
8 25
 
0.8%
9 20
 
0.6%
ValueCountFrequency (%)
2423 1
< 0.1%
2226 1
< 0.1%
2108 1
< 0.1%
2058 1
< 0.1%
2055 1
< 0.1%
1937 1
< 0.1%
1935 1
< 0.1%
1933 1
< 0.1%
1891 1
< 0.1%
1880 1
< 0.1%

6 - 10 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct509
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.22111
Minimum0
Maximum6165
Zeros1398
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:51.615090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q376
95-th percentile541.15
Maximum6165
Range6165
Interquartile range (IQR)76

Descriptive statistics

Standard deviation229.44914
Coefficient of variation (CV)2.43522
Kurtosis164.99319
Mean94.22111
Median Absolute Deviation (MAD)2
Skewness8.191797
Sum291897
Variance52646.907
MonotonicityNot monotonic
2023-07-16T20:00:51.760121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1398
45.1%
1 100
 
3.2%
2 80
 
2.6%
3 49
 
1.6%
4 41
 
1.3%
6 27
 
0.9%
8 26
 
0.8%
5 24
 
0.8%
10 21
 
0.7%
11 21
 
0.7%
Other values (499) 1311
42.3%
ValueCountFrequency (%)
0 1398
45.1%
1 100
 
3.2%
2 80
 
2.6%
3 49
 
1.6%
4 41
 
1.3%
5 24
 
0.8%
6 27
 
0.9%
7 16
 
0.5%
8 26
 
0.8%
9 18
 
0.6%
ValueCountFrequency (%)
6165 1
< 0.1%
2084 1
< 0.1%
1587 1
< 0.1%
1486 1
< 0.1%
1389 1
< 0.1%
1319 1
< 0.1%
1269 1
< 0.1%
1217 1
< 0.1%
1188 1
< 0.1%
1154 1
< 0.1%

10 - 20 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct640
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.44545
Minimum0
Maximum2078
Zeros1207
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:51.913138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q3129.5
95-th percentile763.3
Maximum2078
Range2078
Interquartile range (IQR)129.5

Descriptive statistics

Standard deviation267.54051
Coefficient of variation (CV)1.9607874
Kurtosis7.8862791
Mean136.44545
Median Absolute Deviation (MAD)6
Skewness2.6818101
Sum422708
Variance71577.926
MonotonicityNot monotonic
2023-07-16T20:00:52.051166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1207
39.0%
1 157
 
5.1%
2 57
 
1.8%
3 45
 
1.5%
5 33
 
1.1%
4 30
 
1.0%
10 28
 
0.9%
6 26
 
0.8%
20 23
 
0.7%
11 22
 
0.7%
Other values (630) 1470
47.4%
ValueCountFrequency (%)
0 1207
39.0%
1 157
 
5.1%
2 57
 
1.8%
3 45
 
1.5%
4 30
 
1.0%
5 33
 
1.1%
6 26
 
0.8%
7 22
 
0.7%
8 14
 
0.5%
9 20
 
0.6%
ValueCountFrequency (%)
2078 1
< 0.1%
1822 1
< 0.1%
1802 1
< 0.1%
1759 1
< 0.1%
1673 1
< 0.1%
1672 1
< 0.1%
1665 1
< 0.1%
1601 1
< 0.1%
1517 1
< 0.1%
1462 1
< 0.1%

20 - 30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct382
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.648806
Minimum0
Maximum1560
Zeros1833
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:52.197199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile334.3
Maximum1560
Range1560
Interquartile range (IQR)13

Descriptive statistics

Standard deviation155.20771
Coefficient of variation (CV)2.9479816
Kurtosis23.303603
Mean52.648806
Median Absolute Deviation (MAD)0
Skewness4.4438196
Sum163106
Variance24089.433
MonotonicityNot monotonic
2023-07-16T20:00:52.502267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1833
59.2%
1 166
 
5.4%
2 83
 
2.7%
3 55
 
1.8%
5 40
 
1.3%
4 34
 
1.1%
6 27
 
0.9%
8 18
 
0.6%
12 16
 
0.5%
7 15
 
0.5%
Other values (372) 811
26.2%
ValueCountFrequency (%)
0 1833
59.2%
1 166
 
5.4%
2 83
 
2.7%
3 55
 
1.8%
4 34
 
1.1%
5 40
 
1.3%
6 27
 
0.9%
7 15
 
0.5%
8 18
 
0.6%
9 14
 
0.5%
ValueCountFrequency (%)
1560 1
< 0.1%
1434 1
< 0.1%
1415 1
< 0.1%
1282 1
< 0.1%
1251 1
< 0.1%
1213 1
< 0.1%
1146 1
< 0.1%
1138 1
< 0.1%
1133 1
< 0.1%
1101 1
< 0.1%

>=30 Mbps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct453
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.93706
Minimum0
Maximum11979
Zeros2164
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size24.3 KiB
2023-07-16T20:00:52.656308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile849.2
Maximum11979
Range11979
Interquartile range (IQR)2

Descriptive statistics

Standard deviation479.15078
Coefficient of variation (CV)3.9950187
Kurtosis195.7241
Mean119.93706
Median Absolute Deviation (MAD)0
Skewness11.129189
Sum371565
Variance229585.47
MonotonicityNot monotonic
2023-07-16T20:00:52.815337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2164
69.9%
1 104
 
3.4%
2 59
 
1.9%
3 40
 
1.3%
6 19
 
0.6%
10 18
 
0.6%
4 16
 
0.5%
9 13
 
0.4%
7 12
 
0.4%
8 11
 
0.4%
Other values (443) 642
 
20.7%
ValueCountFrequency (%)
0 2164
69.9%
1 104
 
3.4%
2 59
 
1.9%
3 40
 
1.3%
4 16
 
0.5%
5 11
 
0.4%
6 19
 
0.6%
7 12
 
0.4%
8 11
 
0.4%
9 13
 
0.4%
ValueCountFrequency (%)
11979 1
< 0.1%
8195 1
< 0.1%
7395 1
< 0.1%
6845 1
< 0.1%
6003 1
< 0.1%
5691 1
< 0.1%
4602 1
< 0.1%
4050 1
< 0.1%
3772 1
< 0.1%
2974 1
< 0.1%

Otros
Real number (ℝ)

ZEROS 

Distinct182
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.06133
Minimum-601
Maximum970
Zeros2173
Zeros (%)70.1%
Negative2
Negative (%)0.1%
Memory size24.3 KiB
2023-07-16T20:00:52.964372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-601
5-th percentile0
Q10
median0
Q31
95-th percentile71.15
Maximum970
Range1571
Interquartile range (IQR)1

Descriptive statistics

Standard deviation78.053967
Coefficient of variation (CV)4.859745
Kurtosis61.510148
Mean16.06133
Median Absolute Deviation (MAD)0
Skewness6.9644843
Sum49758
Variance6092.4218
MonotonicityNot monotonic
2023-07-16T20:00:53.116405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2173
70.1%
1 301
 
9.7%
2 102
 
3.3%
3 51
 
1.6%
5 35
 
1.1%
4 32
 
1.0%
10 17
 
0.5%
30 17
 
0.5%
11 14
 
0.5%
20 12
 
0.4%
Other values (172) 344
 
11.1%
ValueCountFrequency (%)
-601 1
 
< 0.1%
-3 1
 
< 0.1%
0 2173
70.1%
1 301
 
9.7%
2 102
 
3.3%
3 51
 
1.6%
4 32
 
1.0%
5 35
 
1.1%
6 11
 
0.4%
7 10
 
0.3%
ValueCountFrequency (%)
970 1
< 0.1%
959 1
< 0.1%
950 1
< 0.1%
850 1
< 0.1%
815 2
0.1%
800 1
< 0.1%
750 2
0.1%
705 1
< 0.1%
688 1
< 0.1%
687 1
< 0.1%

Interactions

2023-07-16T20:00:47.434051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:30.866532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.441475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.950811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:36.837459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.415813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.926666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.383717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.855045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.424040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.957133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.564079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:30.991558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.559509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.064839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:36.955484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.528837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.044691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.498743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.965071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.548069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.076013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.683025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.266620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.694532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.199867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.082513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.655864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.176720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.636774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.094102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.686031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.207114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.908923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.488670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.934601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.419917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.467599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.891423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.406916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.876826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.478184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.934103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.441111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.033033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.596694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.052612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.564960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.577624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.016479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.523523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.990851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.587210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.056123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.551136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.153061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.708719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.178641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.670973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.692649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.131507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.640549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.117879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.701234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.181151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.672174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.277007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.825747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.304673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.791000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.806674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.252509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.760577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.235907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.813959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.309999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.793190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.409022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:31.945365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.432698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:34.915029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:37.925702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.374538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.884611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.363935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:43.935986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.439028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:46.921221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.527084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.054389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.555725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:35.027061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.039727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.487562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:40.999631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.470960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.049009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.559083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.040081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.666116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.187419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.688754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:35.158084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.168757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.625594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.131659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.596989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.181040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.691121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.174098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:48.794104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:32.314447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:33.815781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:35.279110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:38.289782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:39.786630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:41.253687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:42.721015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:44.296013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:45.821082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-16T20:00:47.301127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-16T20:00:53.252444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
id_Provinciaid_Localidadid_PartidoLink Indec<0.512 mbps0.512 - 1 Mbps1 - 6 Mbps6 - 10 Mbps10 - 20 Mbps20 - 30 Mbps>=30 MbpsOtros
id_Provincia1.000-0.029-0.0170.933-0.084-0.022-0.166-0.122-0.131-0.092-0.255-0.024
id_Localidad-0.0291.0000.079-0.092-0.005-0.016-0.0020.0280.0240.0360.0470.002
id_Partido-0.0170.0791.0000.033-0.026-0.1160.0210.035-0.002-0.013-0.0270.047
Link Indec0.933-0.0920.0331.000-0.066-0.003-0.125-0.090-0.090-0.056-0.223-0.032
<0.512 mbps-0.084-0.005-0.026-0.0661.0000.2660.1790.1630.1870.1930.2100.143
0.512 - 1 Mbps-0.022-0.016-0.116-0.0030.2661.0000.2910.2260.2580.1920.2950.210
1 - 6 Mbps-0.166-0.0020.021-0.1250.1790.2911.0000.4580.4000.2630.3190.168
6 - 10 Mbps-0.1220.0280.035-0.0900.1630.2260.4581.0000.5910.4450.3910.192
10 - 20 Mbps-0.1310.024-0.002-0.0900.1870.2580.4000.5911.0000.5740.5390.196
20 - 30 Mbps-0.0920.036-0.013-0.0560.1930.1920.2630.4450.5741.0000.5900.169
>=30 Mbps-0.2550.047-0.027-0.2230.2100.2950.3190.3910.5390.5901.0000.208
Otros-0.0240.0020.047-0.0320.1430.2100.1680.1920.1960.1690.2081.000

Missing values

2023-07-16T20:00:49.000007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T20:00:49.392058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_Provinciaid_Localidadid_PartidoLink Indec<0.512 mbps0.512 - 1 Mbps1 - 6 Mbps6 - 10 Mbps10 - 20 Mbps20 - 30 Mbps>=30 MbpsOtros
008368541002192304725638151860
1062368540100004513000
2011103685402010181010000
30136836854030006600000
4015873685404000172710100
5022743685405000600000
60251936854060002298761423410
702673368540700056825000
803097368540800008518000
9035013685409000033133000
id_Provinciaid_Localidadid_PartidoLink Indec<0.512 mbps0.512 - 1 Mbps1 - 6 Mbps6 - 10 Mbps10 - 20 Mbps20 - 30 Mbps>=30 MbpsOtros
3088233344396900980401075406310000
308923420395901050200031617615130
30902343939590105030000320000
3091231126395901050600000012660
30922333433959010508000702697190133700
3093233717395901051000000004590
3094233184411901120200000313000
309523398041190112030002471187000
30962335874389011902000235329723800
3097233889438901190300029111020541176